Deconstructing TikTok Videos on Mental Health: Cross-sectional, Descriptive Content Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Social media platforms that are based on the creation of visual media, such as TikTok, are increasingly popular with adolescents. Online social media networks provide valuable opportunities to connect with each other to share experiences and strategies for health and wellness. OBJECTIVE: The aim of this study was to describe the content of the hashtag #mentalhealth on TikTok. METHODS: This cross-sectional, descriptive content analysis study included 100 videos with the hashtag #mentalhealth on TikTok. All videos that included the hashtag #mentalhealth were analyzed and coded for the presence of content categories. Additionally, the comments to each video were viewed and coded for content in the following themes: offering support or validation; mentioning experience with suicide or suicidal ideation; mentioning experience with self-harm; describing an experience with hospitalization for mental health issues; describing other mental health issues; and sharing coping strategies, experiences of healing, or ways to feel better. RESULTS: Collectively, the 100 videos studied received 1,354,100,000 views; 266,900,000 likes; and 2,515,954 comments. On average, each video received 13,406,930.69 (SD 8,728,095.52) views; 2,657,425.74 (SD 1,449,920.45) likes; and 24,910.44 (SD 21,035.06) comments. The only content category observed in most (51/100, 51%) of the videos included in the sample was "general mental health." The remaining content categories appeared in less than 50% of the sample. In total, 32% (32/100) of the videos sampled received more than the overall average number of likes (ie, more that 2.67 million likes). Among these 32 videos, 23 (72%) included comments offering support or validation and 20 (62%) included comments that described other mental health issues or struggles. CONCLUSIONS: With over 1 billion cumulative views, almost half of the assessed TikTok videos included in this study reported or expressed symptoms of mental distress. Future research should focus on the potential role of intervention by health care professionals on social media.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.009 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it